The present invention relates to compensation of an unknown channel for a receiver, and more particularly to a two stage adaptive equalizer that provides an efficient algorithm for a batch receiver.
A conventional adaptive equalizer is good for compensating for an unknown channel for a stream type receiver, i.e., a receiver where each received data point or symbol is processed sequentially. The conventional adaptive equalizer can update an equalizing filter symbol by symbol. However a batch receiver stores a certain time interval of received data points or symbols in memory before processing the symbols. If an adaptive equalizer is placed before a batch receiver, the adaptive equalizer is updated once per batch processing, i.e., time interval. This is inefficient. Alternatively updating the adaptive equalizer for every symbol in memory may be possible, but it requires the entire batch processing time for each symbol. This is too time consuming and impractical.
Speed of equalizer convergence for an adaptive equalizer is almost always a concern. The conventional adaptive equalizer is designed for stream receivers, as shown in FIG. 1. A transmitter provides a signal that passes through an unknown channel and is then sampled at the input of the stream receiver. A post signal processing block performs signal corrections, such as timing and phase adjustments.
An example of a batch receiver is a modulation analysis system found in a Real Time Spectrum Analyzer (RTSA), such as those manufactured by Tektronix, Inc. of Beaverton, Oreg. The entire batch processing is used to demodulate and analyze the received signals. Many times it is desired to add an adaptive equalizer to the batch receiver without modifying the batch processing block. A typical configuration for a batch receiver without an adaptive equalizer is shown in FIG. 2 where the data points output from the sampler are stored in a memory. However, to provide for adaptive equalization in this situation, as shown in FIG. 3, the adaptive equalizer is placed after the memory, but before the batch processing. Symbol by symbol adaptation is not practical, and batch by batch adaptation needs to be used. A problem with this equalization system using a conventional adaptive equalizer is slow convergence because the filter taps are updated only once per batch based upon a single data symbol from the batch receiver output.
The response of the unknown channel is h, and the adaptation algorithm tries to find equalizing filter, g, so that h*g=δ, where δ is a Kronecker delta or impulse sequence in discrete time and * indicates convolution. Although the adaptation is not perfect, eventually the adaptation is close enough to hold the equation after a certain number of iterations. However the conventional adaptive equalizer shown in FIG. 4, when applied in a batch receiver, takes time to converge because the update rate is much less than in the stream receiver shown in FIG. 1.
What is desired is a adaptive equalizer for a batch receiver that achieves a faster equalizer convergence than a conventional adaptive equalizer.